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Monday, February 16, 2015 - 4:15pm to 5:30pm
Random Subdivisions & Neural Coding |
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Monday, March 15, 2021 - 4:00pm
Title: Data-Driven Transfer of Insight between Brains and AI Systems |
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Monday, December 4, 2023 - 4:00pm to 5:00pm
Inference and sampling via diffusion processes |
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Thursday, May 7, 2015 - 8:45am to Saturday, May 9, 2015 - 12:00pm
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Monday, October 28, 2013 - 4:15pm to 5:30pm
Pseudo-likelihood methods for community detection in large sparse networks |
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Thursday, January 16, 2020 - 4:00pm to 5:00pm
Generalization Error of Linearized Neural Networks: Staircase and Double-descent. |
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Monday, January 23, 2023 - 4:00pm to 5:00pm
Title: Universality phenomena in high-dimensions |
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Monday, September 25, 2017 - 4:15pm to 5:30pm
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Monday, April 1, 2013 - 4:15pm to 5:30pm
Linear methods for large regressions |
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Friday, April 19, 2019 - 11:00am to 12:00pm
Adaptive Deep Representation Learning for Understanding Video and Natural Language |
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Monday, February 28, 2022 - 4:00pm to 5:00pm
Title: What Can Conformal Inference Offer To Statistics? |
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Monday, December 14, 2015 - 4:15pm to 5:30pm
Statistical Estimation with Random Forests |
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Friday, February 20, 2015 - 11:30am to 12:30pm
Approximate Bayesian Computation for the stellar initial mass function |
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Monday, March 8, 2021 - 4:00pm to 5:00pm
Title: Taming Nonconvexity in Statistical and Reinforcement Learning |
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Monday, January 22, 2024 - 4:00pm to 5:00pm
Toward a practical theory of deep learning |
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Friday, December 11, 2015 - 3:00pm to 5:00pm
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Monday, November 11, 2013 - 4:15pm to 5:30pm
Local Privacy and Statistical Minimax Rates |
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Monday, February 3, 2020 - 4:00pm to 5:00pm
Foundations of Learning Systems with (Deep) Function Approximators |
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Monday, January 30, 2023 - 4:00pm to 5:00pm
Title: Machine learning for precision medicine |
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Friday, September 8, 2017 - 11:00am
Nonparametric density estimation for intentionally corrupted functional data |
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Monday, April 15, 2013 - 4:15pm to 5:30pm
Large Average Submatrices of a Gaussian Random Matrix: Landscapes and Local Optima |
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Monday, April 1, 2019 - 4:00pm to 5:15pm
Three principles of data science: predictability, computability, and stability (PCS) |
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Monday, February 14, 2022 - 4:00pm to 5:00pm
Title: What can statisticians learn from the analysis of C.elegans data? |
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Tuesday, January 26, 2016 - 12:00pm to 1:15pm
Measuring Sample Quality with Stein's Method |
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Monday, February 9, 2015 - 4:15pm to 5:30pm
Convex Clustering |
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Friday, April 2, 2021 - 11:30am to 12:30pm
Title: Semiparametric Proximal Causal Inference |
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Monday, April 1, 2024 - 4:00pm to 5:00pm
Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation |
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Friday, October 20, 2017 - 9:00am to 5:00pm
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Monday, November 18, 2013 - 4:15pm to 5:30pm
From compression to compressed sensing |
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Monday, February 17, 2020 - 4:00pm to 5:00pm
The Blessings of Multiple Causes |
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Wednesday, February 1, 2023 - 4:00pm to 5:00pm
Title: Towards the Statistically Principled Design of ML Algorithms |
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Monday, September 18, 2017 - 4:15pm to 5:30pm
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Monday, December 3, 2012 - 4:15pm to 5:30pm
Covariance and Precision Matrix Estimation for High-Dimensional Time Series |
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Monday, January 14, 2019 - 3:45pm to 5:00pm
Statistics and Data Science Project Pitch |
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Wednesday, February 16, 2022 - 4:00pm to 5:00pm
Title: Spectral Independence: A New Tool to Analyze Markov Chains |
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Monday, February 1, 2016 - 4:15pm to 5:30pm
Multiple Testing and Adaptive Estimation via the Sorted L-One Norm |
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Monday, April 13, 2015 - 4:15pm to 5:30pm
Algorithms for Lipschitz Learning on Graphs |
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Monday, May 17, 2021 - 4:00pm to 5:00pm
Title: From Diagnosis to Treatment - Augmenting Clinical Decision Making with Artificial Intelligence |
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Wednesday, March 6, 2024 - 12:00pm to 1:00pm
Learning from Dynamics |
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Monday, April 30, 2018 - 3:00pm to 5:00pm
First Annual S&DS Poster Session |
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Monday, December 9, 2013 - 4:15pm to 5:30pm
Title: (M)-estimation Under 'Big Data' Asymptotics: The Extra Noise Phenomenon characterized by Approximate Message Passing |
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Monday, February 24, 2020 - 4:00pm to 5:00pm
Data science methods to reduce inequality and improve healthcare |
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Monday, February 6, 2023 - 4:00pm to 5:00pm
Title: From HeartSteps to HeartBeats: Personalized Decision-making |
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Monday, October 9, 2017 - 4:15pm to 5:30pm
Boosting hazard regression with time-varying covariates |
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Monday, January 28, 2013 - 4:15pm to 5:30pm
Structured Estimation in High-Dimensions |
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Tuesday, January 15, 2019 - 4:00pm to 5:30pm
Reverse-Engineering Cognition with Causal Generative Models and Deep Neural Networks |
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Wednesday, February 2, 2022 - 4:00pm to 5:00pm
Title: Targeting climate science and data science at policies |
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Friday, February 5, 2016 - 12:00pm to 1:15pm
Nonparametric Statistical Inference on non-Euclidean spaces |
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Wednesday, April 15, 2015 - 4:15pm to 5:30pm
Locally Stationary Latent Factors |
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Monday, May 3, 2021 - 4:00pm to 5:00pm
Solving PDEs with Deep Learning |
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Monday, April 8, 2024 - 4:00pm to 5:00pm
Robust Statistics in High Dimension |
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Friday, October 26, 2018 - 12:00pm to 1:15pm
Heterogeneous Data: Invariance, Causality and Novel Robustness |
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Monday, January 27, 2014 - 4:15pm to 5:30pm
Feature allocations, paintboxes, and probability functions |
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Wednesday, February 26, 2020 - 4:00pm to 5:00pm
Designing for the Last Mile in Machine Learning |
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Wednesday, February 8, 2023 - 4:00pm to 5:00pm
Title: What, When, and How can we Learn Adversarially Robustly? |
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Monday, November 13, 2017 - 4:15pm to 5:30pm
A basic learning problem that is independent of the set theory ZFC axioms |
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Thursday, January 31, 2013 - 4:15pm to 5:30pm
Minimax estimation of high-dimensional predictive densities |
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Wednesday, February 27, 2019 - 4:00pm to 5:15pm
A modern maximum-likelihood approach for high-dimensional logistic regression |
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Monday, February 21, 2022 - 4:00pm to 5:00pm
Title: Learning to Generate Data by Estimating Gradients of the Data Distribution |
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Monday, February 8, 2016 - 4:15pm to 5:30pm
High-dimensional regression in scientific work: improving precision of causal estimates using many covariates, and building encoding models of the extrastriate visual cortex |
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Friday, May 1, 2015 - 3:15pm to 4:30pm
Asymptotic Normality, Rates of Convergence, and Large Deviation Probabilities for a Broad Class of Statistics |
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Monday, September 20, 2021 - 4:00pm to 5:00pm
Title: Mean Field Approximation in Bayesian Linear Regression |
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Monday, January 29, 2024 - 4:00pm to 5:00pm
Mathematical Foundations for Physical Agents |
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Monday, September 10, 2018 - 4:15pm to 5:30pm
First Statistics and Data Science Project Pitch |
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Monday, January 13, 2014 - 4:15pm to 5:30pm
Computation, Communication, and Privacy Constraints on Statistical Estimation |
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Monday, March 2, 2020 - 4:00pm to 5:00pm
Deep Probabilistic Graphical Modeling |
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Monday, February 13, 2023 - 4:00pm to 5:00pm
Searching for the implicit bias of deep learning |
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Monday, October 2, 2017 - 4:15pm to 5:30pm
Robustness Meets Algorithms |
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Monday, October 23, 2017 - 4:30pm to 5:45pm
Transformational Power of Big Data & Cognitive Computing:Perspectives from IBM |
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Thursday, January 17, 2013 - 4:15pm to 5:30pm
Testing of Large Covariance Matrices |
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Monday, January 28, 2019 - 4:00pm to 5:15pm
Neural data science: From recordings to theoretical models |
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Wednesday, February 23, 2022 - 4:00pm to 5:00pm
Title: Incentive-Aware Machine Learning for Decision Making |
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Monday, February 22, 2016 - 4:15pm to 5:30pm
Fast Bayesian Factor Analysis via Automatic Rotations to Sparsity |
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Friday, September 4, 2015 - 12:00pm to 1:15pm
Asymptotic theory for the Rasch model |
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Monday, September 13, 2021 - 4:00pm to 5:00pm
Title: Reinforcement Learning in High Dimensional Systems |
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Monday, February 12, 2024 - 4:00pm to 5:00pm
Fine-Grained Extensions of the Low-Degree Testing Framework |
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Monday, September 24, 2018 - 4:15pm to 5:30pm
Crowdsourcing Regression: Making accurate predictions while knowing almost nothing |
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Friday, January 31, 2014 - 12:00pm to 1:15pm
Optimal Shrinkage of Singular Values for Matrix Denoising |
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Monday, September 14, 2020 - 4:00pm to 5:15pm
Title: Dynamical systems and machine learning: combining in a principled way data-driven models and domain-driven models |
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Wednesday, February 15, 2023 - 4:00pm to 5:00pm
Title: Towards a theory of complexity of sampling, inspired by optimization |
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Monday, February 3, 2014 - 4:15pm to 5:30pm
Reasoning about uncertainty in high-dimensional data analysis |
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Monday, September 21, 2020 - 4:00pm to 5:00pm
Title: Off-policy Estimation by the Regularized Lagrangian |
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Monday, February 20, 2023 - 4:00pm to 5:00pm
Title: Stable Variable Selection with Knockoffs |
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Monday, November 27, 2017 - 4:15pm to 5:30pm
Transcriptome Mining: Tackling core issues related to gene regulation & also analyzing the "data exhaust" associated with this activity |
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Monday, January 14, 2013 - 4:15pm to 5:30pm
Algorithms for Interpretable Machine Learning |
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Thursday, February 14, 2019 - 4:00pm to 5:15pm
A Computational Approach to Emotion |
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Monday, February 7, 2022 - 4:00pm to 5:00pm
Title: Identifying Prediction Mistakes in Observational Data |
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Monday, April 18, 2016 - 4:15pm to 5:30pm
Statistical Science and Policy at the EPA |
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Monday, November 16, 2015 - 4:15pm to 5:30pm
Network driven sampling; a critical threshold for design effects |
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Monday, September 27, 2021 - 4:00pm to 5:00pm
Title: DNA Copy Number Profiling from Bulk Tissues to Single Cells |
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Monday, February 5, 2024 - 4:00pm to 5:00pm
Analyzing Data with Systematic Bias: Truncation and Self-Selection |
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Monday, December 3, 2018 - 4:15pm to 5:30pm
Machine Learning: Dynamical, Economic and Stochastic Perspectives |
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Monday, January 20, 2014 - 4:15pm to 5:30pm
Bayesian inference as iterated random functions with applications to sequential inference in graphical models |
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Monday, September 28, 2020 - 4:00pm to 5:00pm
Title: Is distribution-free inference possible for binary regression? |
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Wednesday, February 22, 2023 - 4:00pm to 5:00pm
Learning structured representations for accelerating scientific discovery and simulation |
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Monday, December 4, 2017 - 4:15pm to 5:30pm
Learning Determinantal Point Processes and Applications |
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Monday, March 25, 2013 - 4:15pm to 5:30pm
Shaping Cities of the Future using Mobile Data |
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Monday, March 4, 2019 - 4:00pm to 5:15pm
Quantifying literary style and evolution |
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Wednesday, February 9, 2022 - 4:00pm to 5:00pm
Title: Bridging the Gap Between Deep Learning and Probabilistic Modeling |
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Friday, April 8, 2016 - 10:30am to 12:00pm
Inferential Thinking: Data Science Education for Berkeley Undergraduates |
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Monday, November 2, 2015 - 4:15pm to 5:30pm
A stochastic rank ordered logit model for rating multi-competitor games and sports |
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Monday, October 25, 2021 - 4:00pm to 5:00pm
Title: Deep Networks and the Multiple Manifold Problem |
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Monday, April 29, 2024 - 4:00pm to 5:00pm
Title: TBA |
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Monday, November 26, 2018 - 4:15pm to 5:30pm
Nanostatistics |
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Monday, February 17, 2014 - 4:15pm to 5:30pm
Nonconvex methods for high-dimensional regression with noisy and missing data |
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Monday, October 19, 2020 - 4:00pm to 5:00pm
Separating Estimation from Decision Making in Contextual Bandits |
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Monday, February 27, 2023 - 4:00pm to 5:00pm
Title: Physics-informed deep learning: Blending data and physics for learning functions and operators |
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Friday, November 10, 2017 - 11:00am to 1:00pm
Permutation-based models for ranking from pairwise comparisons |
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Monday, February 4, 2013 - 4:15pm to 5:30pm
Association studies with functional phenotypes |
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Monday, February 11, 2019 - 4:00pm to 5:15pm
The diffusion of opioids in the family |
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Monday, March 14, 2022 - 4:00pm to 5:00pm
Title: Theoretical Foundations of Pre-trained Models |
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Monday, May 2, 2016 - 4:15pm to 5:30pm
Provable algorithms for learning neural networks |
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Monday, April 4, 2016 - 4:15pm to 5:30pm
Statistical Methods for Studying Ice Sheets |
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Monday, November 1, 2021 - 4:00pm to 4:45pm
Title: Analysis of gradient descent on wide two-layer ReLU neural networks |
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Monday, March 4, 2024 - 4:00pm to 5:00pm
Inferential Machine Learning: Statistics, Data Science, and Public Policy |
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Wednesday, September 12, 2018 - 12:00pm to 1:00pm
Improving Generative Adversarial Networks using Game Theory and Statistics |
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Monday, April 7, 2014 - 4:15pm to 5:30pm
Learning Bayesian networks based on sparsest permutations |
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Monday, October 26, 2020 - 4:00pm to 5:00pm
Some Recent Insights on Transfer-Learning |
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Wednesday, March 1, 2023 - 4:00pm to 5:00pm
Title: The Power and Limitations of Convexity in Data Science |
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Monday, November 6, 2017 - 4:15pm to 5:30pm
Optimal hypothesis testing for stochastic block models with growing degrees |
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Monday, February 11, 2013 - 4:15pm to 5:30pm
Modeling Heterogeneity within Multiway Array Data |
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Thursday, February 21, 2019 - 4:00pm to 5:15pm
Exploiting computational scale for richer model-based inference |
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Wednesday, March 2, 2022 - 4:00pm to 5:00pm
Title: Algorithmic Thresholds in Mean-Field Spin Glasses |
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Monday, November 28, 2016 - 4:15pm to 5:30pm
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments |
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Monday, October 26, 2015 - 4:15pm to 5:30pm
On the Nyström and Column-Sampling Methods for the Approximate Principal Components Analysis of Large Data Sets |
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Monday, October 4, 2021 - 4:00pm to 5:00pm
Title: Big Data Analytics Applied to the Molecular Basis of Human Traits and Diseases |
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Monday, March 25, 2024 - 4:00pm to 5:00pm
Harvesting randomness to understand computational social systems |
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Monday, October 22, 2018 - 4:15pm to 5:30pm
Learning Graph Inverse Problems with Geometric Neural Networks |
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Monday, March 24, 2014 - 4:15pm to 5:30pm
Network histograms and universality of blockmodel approximation |
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Monday, November 30, 2020 - 4:00pm to 5:00pm
Insights from Deep Representations for Machine Learning Systems and Human Collaborations |
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Monday, March 6, 2023 - 4:00pm to 5:00pm
New statistical and computational phenomena from deep learning |
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Thursday, January 18, 2018 - 4:15pm to 5:15pm
Automated, Scalable Bayesian Inference with Theoretical Guarantees |
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Monday, February 18, 2013 - 4:15pm to 5:30pm
Estimation of Probabilities in Large Incomplete Contingency Tables Using Semi-Parametric Mixture Models |
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Thursday, January 24, 2019 - 4:00pm to 5:15pm
On the Foundations of Deep Learning: SGD, Overparametrization, and Generalization |
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Monday, March 7, 2022 - 4:00pm to 5:00pm
Title: Reliable machine learning in the wild |
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Monday, October 31, 2016 - 4:15pm to 5:30pm
Learning a Tree-Structured Ising Model in Order to Make Predictions |
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Monday, September 14, 2015 - 4:15pm to 5:30pm
Information theory of algorithms: How precisely should we compute? |
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Monday, October 18, 2021 - 4:00pm to 5:00pm
Title: Towards practical estimation of Brenier maps |
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Monday, April 22, 2024 - 4:00pm to 5:00pm
Analyzing genetic summary data: statistical models and platforms |
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Monday, September 17, 2018 - 4:15pm to 5:30pm
Accuracy of High-Dimensional Deep Learning Networks |
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Monday, April 14, 2014 - 4:15pm to 5:30pm
Learning from Big Data in biology |
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Monday, November 9, 2020 - 4:00pm to 5:00pm
Replication and Evidence Factors in Observational Studies |
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Wednesday, March 8, 2023 - 4:00pm to 5:00pm
Statistically Efficient Offline Reinforcement Learning and Causal Machine Learning |
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Monday, January 29, 2018 - 4:15pm to 5:30pm
Eigenvalues in multivariate random effects models |
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Monday, February 25, 2013 - 4:15pm to 5:30pm
Nonparametric methods for spot volatility estimation under measurement noise |
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Monday, February 4, 2019 - 4:00pm to 5:15pm
Bridging MCMC and Optimization |
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Wednesday, March 9, 2022 - 4:00pm to 5:00pm
Title: Understanding Statistical-vs-Computational Tradeoffs via Low-Degree Polynomials |
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Monday, September 12, 2016 - 4:15pm to 5:30pm
A Variational Algorithm for Bayesian Variable Selection |
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Friday, October 2, 2015 - 12:00pm to 1:15pm
Recent advances in selective inference |
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Monday, November 8, 2021 - 4:00pm to 5:00pm
Title: On Bias and Discretization: Sampling under Isoperimetry via Langevin Algorithm |
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Monday, April 15, 2024 - 4:00pm to 5:00pm
Strategic Design to Improve Information Integrity |
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Monday, October 8, 2018 - 4:15pm to 5:30pm
Random initialization and implicit regularization in nonconvex statistical estimation |
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Monday, April 28, 2014 - 4:15pm to 5:30pm
Minimax Optimality in Nonparametric Statistics and Machine Learning |
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Monday, November 16, 2020 - 4:00pm to 5:00pm
How Do Our ML Models Succeed? |
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Thursday, February 23, 2023 - 10:30am to 11:30am
The price of computational efficiency in high-dimensional estimation |
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Monday, February 26, 2018 - 4:15pm to 5:15pm
Interactive algorithms for multiple hypothesis testing |
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Thursday, February 7, 2013 - 4:15pm to 5:30pm
Learning and the Topology of Social Networks |
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Monday, February 25, 2019 - 4:00pm to 5:15pm
Better understanding of modern paradigms in probabilistic models |
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Wednesday, March 16, 2022 - 4:00pm to 5:00pm
Title: Latent Confounding Adjustment with Text: Opportunities and Limitations |
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Monday, April 3, 2017 - 12:00pm to 1:15pm
Persistence diagrams classification using a point process distance: an application to signals |
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Friday, August 21, 2015 - 11:00am to 1:00pm
Tensor Eigenvalue and Its Applications |
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Monday, October 11, 2021 - 4:00pm to 5:00pm
Title: Reaching consensus: the power of few |
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Monday, April 8, 2024 - 2:30pm to 3:30pm
Causal Effect Estimation with Context and Confounders |
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Monday, November 12, 2018 - 4:15pm to 5:30pm
The Blessings of Multiple Causes |
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Monday, September 8, 2014 - 4:15pm to 5:30pm
Marcenko-Pastur Law for Tyler's and Maronna's M-estimators |
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Monday, November 2, 2020 - 4:00pm to 5:00pm
Opening the Black Box: Understanding Deep Learning via Analyzing Trajectories of Gradient Descent |
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Monday, March 27, 2023 - 4:00pm to 5:00pm
What Makes Data Suitable for Deep Learning? |
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Monday, February 5, 2018 - 4:15pm to 5:15pm
Fairness and Diversity in Online Social Systems |
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Monday, September 10, 2012 - 4:15pm to 5:30pm
Can Cell Morphology tell a story? |
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Monday, February 18, 2019 - 4:00pm to 5:15pm
Deep Learning for Network Biomedicine |
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Friday, April 1, 2022 - 1:00pm to 2:00pm
Title: “Partisans, Racialists, and Neutrals: Investigating the Interdependence of Attitudes towards Social Groups” |
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Monday, October 10, 2016 - 4:15pm to 5:30pm
Information-theoretic bounds and phase transitions in clustering, sparse PCA, and submatrix localization |
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Monday, October 12, 2015 - 4:15pm to 5:30pm
A Bayes Framework for Complex Linear Models |
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Monday, November 29, 2021 - 4:00pm to 5:00pm
Title: Kernel Thinning and Stein Thinning |
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Monday, December 10, 2018 - 4:15pm to 5:30pm
Towards understanding prediction performance of neural networks |
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Wednesday, April 16, 2014 - 2:30pm to 3:45pm
Weighted likelihood estimation under two-phase sampling |
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Monday, December 7, 2020 - 4:00pm to 5:00pm
Tradeoffs in Adversarial Training |
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Wednesday, March 29, 2023 - 4:00pm to 5:00pm
Interpolation Learning and Overfitting with Linear Predictors and Short Programs |
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Thursday, February 1, 2018 - 4:15pm to 5:15pm
A lens into cognition: The geometry and topology of neural systems |
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Monday, September 17, 2012 - 4:15pm to 6:30pm
Regularization for large-scale regression |
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Thursday, April 25, 2019 - 4:00pm to 5:15pm
Online Lipschitz Selection |
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Monday, November 7, 2022 - 4:00pm to 5:00pm
Sharp Matrix Concentration |
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Monday, September 26, 2016 - 4:15pm to 5:30pm
Modeling disease propagation in networks: Source-finding and influence maximization |
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Monday, November 9, 2015 - 4:15pm to 5:30pm
Estimating causal effects in the presence of interfering units |
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Monday, December 6, 2021 - 4:00pm to 5:00pm
Title: Minimax rates for sparse signal detection under correlation |
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Monday, October 15, 2018 - 4:15pm to 5:30pm
Sparse Matrices in Sparse Analysis |
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Tuesday, December 11, 2018 - 4:00pm to 5:30pm
Learning from Highly Correlated Features using Graph Total Variation |
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Monday, April 21, 2014 - 4:15pm to 5:30pm
The statistical price to pay for computational efficiency in sparse PCA |
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Monday, December 14, 2020 - 4:00pm to 5:00pm
Learning interactive and robust models from additional human supervision |
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Monday, April 3, 2023 - 4:00pm to 5:00pm
Conditional Gradients in Machine Learning |
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Monday, February 12, 2018 - 4:15pm to 5:15pm
Pixels to predictions: a unified framework for image analysis of particle motion in micron scale environments using mechanistic modeling and machine learning |
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Monday, September 24, 2012 - 4:15pm to 5:30pm
Combining nonparametric inferences using data depth, bootstrap and confidence distribution |
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Monday, April 8, 2019 - 4:00pm to 5:15pm
Identifying disease-relevant cell types from genome-wide association data |
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Monday, October 17, 2022 - 4:00pm to 5:00pm
Title: Thompson Sampling-Guided Directed Evolution for Sequence Optimization |
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Monday, November 7, 2016 - 4:15pm to 5:30pm
New Results at the Crossroads of Convexity, Learning and Information Theory |
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Monday, November 30, 2015 - 4:15pm to 5:30pm
Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers |
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Monday, November 15, 2021 - 4:00pm to 5:00pm
Title: Algorithmic Thresholds for Optimizing Mean-Field Spin Glasses |
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Monday, December 7, 2015 - 4:15pm to 5:30pm
Polynomial approximation, moment matching and optimal estimation of the unseen |
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Monday, December 13, 2021 - 4:00pm to 5:00pm
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Monday, October 1, 2018 - 12:00pm
YINS Industry Seminar: Enigma “Linking Data For The Greater Good” |
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Monday, September 15, 2014 - 4:15am
Good Margins Make Good Neighbors |
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Monday, October 12, 2020 - 4:00pm to 5:00pm
Tradeoffs between Robustness and Accuracy |
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Wednesday, April 5, 2023 - 4:00pm to 5:00pm
Stripping the Discount Curve – a Robust Machine Learning Approach |
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Thursday, February 15, 2018 - 4:15pm to 5:15pm
Studies of the microbiome, the complex communities of bacteria that live in and around us, present interesting statistical problems. |
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Monday, October 1, 2012 - 4:15pm to 5:30pm
Observational studies in healthcare: are they any good? |
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Monday, April 15, 2019 - 4:00pm to 5:15pm
Physics-Inspired Algorithms for Sampling |
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Tuesday, August 30, 2022 - 3:00pm to 4:00pm
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Monday, October 17, 2016 - 4:15pm to 5:30pm
Generalized fiducial Inference: A Review |
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Friday, December 11, 2015 - 3:00pm to 5:00pm
Likelihood ratios for eigenvalues in spiked multivariate models |
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Thursday, January 20, 2022 - 4:00pm to 5:00pm
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Monday, October 29, 2018 - 4:15pm to 5:30pm
Opportunities for Statistics in Implementation Science at Yale’s new Center for Methods in Implementation and Prevention Science (CMIPS) |
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Monday, September 22, 2014 - 4:15pm to 5:30pm
Mathematical Models of the Structure and Function of Social Networks |
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Monday, October 5, 2020 - 4:00pm to 5:00pm
Title: Structural Foundations of Efficient Reinforcement Learning |
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Monday, April 17, 2023 - 4:00pm to 5:00pm
A Fruitful Reciprocity: The Neuroscience-AI Connection |
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Monday, February 19, 2018 - 4:15pm to 5:15pm
Inverse Problems and Unsupervised Learning with applications to Cryo-Electron Microscopy |
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Monday, October 8, 2012 - 4:15pm to 5:30pm
Stability Selection in Cluster Analysis |
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Thursday, May 2, 2019 - 3:00pm to 5:00pm
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Monday, October 3, 2022 - 4:00pm to 5:00pm
Title: Spectral and post-spectral estimators for grouped panel data models |
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Monday, October 24, 2016 - 4:15pm to 5:30pm
Interpretable prediction models for network-linked data |
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Monday, April 25, 2016 - 4:15pm to 5:30pm
Recent Advances in Trend Filtering |
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Monday, January 31, 2022 - 4:00pm to 5:00pm
Title: Data science and policy: Addressing inequity in health |
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Monday, November 5, 2018 - 4:15pm to 5:30pm
Locally Stationary Spatio-Temporal Interpolation of Argo Profiling Float Data |
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Monday, September 29, 2014 - 4:15pm to 5:30pm
From Scattering to Spectral Networks |
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Wednesday, January 13, 2021 - 10:00am to 11:00am
Title: Asymptotics of learning on dependent and structured random objects |
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Thursday, April 20, 2023 - 4:00pm to 5:00pm
Statistical applications of Wasserstein gradient flows |
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Thursday, March 1, 2018 - 12:00pm to 1:30pm
The Design of Field Experiments With Survey Outcomes: Applications to the Study of the Persuasive Effects of Campaign Contact |
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Monday, October 15, 2012 - 4:15pm to 5:30pm
Distributions of Angles in Random Packing on Spheres. |
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Monday, September 23, 2019 - 4:00pm to 5:15pm
Asymptotic Bayes risk for Gaussian mixture in a semi-supervised setting |
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Monday, November 14, 2022 - 4:00pm to 5:00pm
Title: Confidence Intervals for Nonparametric Empirical Bayes Analysis |
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Tuesday, November 15, 2016 - 11:45am to 1:00pm
How Well Do Local Algorithms Solve Semidefinite Programs? |
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Monday, October 27, 2014 - 4:15pm to 5:30pm
Genetics and Geography: Using genomic data to infer the fine-scale population structure and population history of the people of the British Isles |
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Thursday, January 14, 2021 - 11:00am to 12:00pm
Title: Machine learning and causality: Building efficient, reliable models for decision-making |
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Monday, May 8, 2023 - 5:15pm to 7:00pm
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Monday, March 5, 2018 - 4:15pm to 5:15pm
The great power of AI: Algorithmic mirrors of individuals and society |
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Monday, April 8, 2013 - 4:15pm to 5:30pm
D2-Clustering and Distance-Based Mixture Modeling with Applications |
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Tuesday, October 22, 2019 - 4:00pm to 5:15pm
Statistical guarantees for the Bayesian approach to inverse problems |
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Monday, September 26, 2022 - 4:00pm to 5:00pm
Title: Privacy of Noisy SGD |
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Tuesday, November 29, 2016 - 4:00pm
The cost and benefit of reducing Big Data size and complexity |
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Monday, October 6, 2014 - 4:15pm to 5:30pm
Statistical Estimation of Latent Variable Models via Tensor Decompositions |
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Wednesday, January 20, 2021 - 9:45am to 10:45am
Title: Breaking the Sample Size Barrier in Reinforcement Learning |
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Monday, September 25, 2023 - 4:00pm to 5:00pm
Title: The unreasonable effectiveness of negative association |
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Monday, April 9, 2018 - 4:15pm to 5:30pm
On Spectral Graph Clustering |
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Tuesday, April 23, 2013 - 4:15pm to 5:30pm
Matrix Completion and Matrix Concentration |
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Monday, September 9, 2019 - 4:00pm to 5:15pm
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Monday, September 19, 2022 - 4:00pm to 5:00pm
Title: Sampling from the SK measure via algorithmic stochastic localization |
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Friday, December 9, 2016 - 11:00am to 1:00pm
How to discover a hidden structure of a complex network |
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Monday, October 20, 2014 - 4:15pm to 5:30pm
An introduction to multiway component analysis (a.k.a. tensor decomposition) |
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Monday, January 18, 2021 - 9:45am to 10:45am
Title: Ideal made real: machine learning with limited data and interpretable outputs |
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Monday, November 27, 2023 - 4:00pm to 5:00pm
Title: Representational strengths and limitations of transformers |
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Monday, April 2, 2018 - 4:15pm to 5:30pm
Pivotal Estimation and Confidence Bands for High Dimensional Linear Models with Error-in-variables |
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Monday, April 29, 2013 - 4:15pm to 5:30pm
On the conditional distributions of low-dimensional projections from high-dimensional data |
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Monday, December 9, 2019 - 3:45pm to 5:15pm
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Monday, December 5, 2022 - 4:00pm to 5:00pm
Title: Testing the stability of a black-box algorithm |
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Monday, April 24, 2017 - 4:15pm to 5:30pm
Sketchy decisions: Low-rank matrix optimization with optimal storage |
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Monday, November 17, 2014 - 4:15pm to 5:30pm
Scalable Bayes via Barycenter Posteriors |
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Tuesday, January 19, 2021 - 9:45am to 10:45am
Title: VCBART: Bayesian trees for varying coefficients |
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Monday, September 11, 2023 - 4:00pm to 5:00pm
Title: Clip-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments |
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Friday, April 27, 2018 - 11:00am to 1:00pm
The Library of Babel: On Trying to Read My Genome |
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Monday, May 6, 2013 - 4:15pm to 5:30pm
Inferring Functional Interaction and Transition Patterns via Dynamic Bayesian Variable Partition Model / Multiple Bayesian Partition models |
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Monday, October 14, 2019 - 4:00pm to 5:15pm
Model Selection for Machine Learning of Doubly Robust Functionals |
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Monday, September 12, 2022 - 4:00pm to 5:00pm
Title: Efficient Rich-observation Reinforcement Learning: A Representation Learning Approach |
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Friday, February 10, 2017 - 11:00am to 1:00pm
Agnostic Notes on Regression Adjustments to Experimental Data: Reexamining Freedman’s Critique |
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Monday, November 10, 2014 - 4:15pm to 5:30pm
Statistical Challenges in Epigenomics: Detecting Differentially Methylated Regions in the Presence of Unwanted Variability |
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Monday, March 29, 2021 - 4:00pm to 5:00pm
Title: Mobility Networks for Modeling the Spread of COVID-19: Explaining infection rates and informing reopening strategies |
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Monday, November 13, 2023 - 4:00pm to 5:00pm
Title: Sample Complexity of Q-learning: from Single-agent to Federated Learning |
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Friday, April 13, 2018 - 11:00am to 1:00pm
Non-Convex Empirical Risk Minimization via the Langevin Algorithm |
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Monday, September 30, 2013 - 4:15pm to 5:30pm
Shaping Cities of the Future using Mobile Data |
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Monday, October 7, 2019 - 4:00pm to 5:15pm
TBA |
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Monday, October 24, 2022 - 4:00pm to 5:00pm
Title: Markovian Linear Stochastic Approximation: Bias and Extrapolation |
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Monday, February 20, 2017 - 4:15pm to 5:30pm
Social Network Experiments |
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Monday, October 13, 2014 - 4:15pm to 5:30pm
Estimating Average Causal Effects Under Interference (coauthored with Cyrus Samii, NYU) |
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Thursday, January 21, 2021 - 11:00am to 12:00pm
Title: Demystifying the Sample Efficiency of (Deep) Reinforcement Learning |
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Monday, September 18, 2023 - 4:00pm to 5:00pm
Title: Learning flows for generating and transferring data: An embarrassingly simple approach |
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Wednesday, April 18, 2018 - 12:00pm to 1:00pm
Toward Theoretical Understanding of Deep Learning |
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Monday, October 7, 2013 - 4:15pm to 5:30pm
Non-asymptotic theory of random matrices and covariance estimation |
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Monday, November 18, 2019 - 4:00pm to 5:15pm
Recent Developments in the Study of Single-Index Type Models |
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Monday, November 28, 2022 - 4:00pm to 5:00pm
Title: Testing thresholds in high-dimensional random geometric graphs |
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Friday, February 24, 2017 - 11:30am to 12:45pm
Differentially Private Statistical Inference |
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Monday, December 1, 2014 - 4:15pm to 5:30pm
Optimal Testing for Planted Satisfiability Problems |
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Monday, January 25, 2021 - 1:00pm to 2:00pm
Title: Positive AI with Social Commonsense Models |
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Monday, October 2, 2023 - 4:00pm to 5:00pm
Title: On the "chemistry" of deep learning: lessons learned from training 10^8 networks on toy problems |
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Tuesday, April 24, 2018 - 12:00pm to 1:15pm
Optimization's Hidden Gift to Learning: Implicit Regularization |
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Monday, September 9, 2013 - 4:15pm to 5:30pm
Nonparametric Inference and the Bernstein-von Mises phenomenon |
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Monday, September 30, 2019 - 4:00pm to 5:15pm
Neural SDEs: Deep Generative Models in the Diffusion Limit |
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Monday, October 31, 2022 - 4:00pm to 5:00pm
Title: Machine learning for determining protein structure and dynamics from cryo-EM images |
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Friday, March 10, 2017 - 11:30am to 12:45pm
People in context: Social understanding though linguistic and network analysis |
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Monday, December 8, 2014 - 4:15pm
Semi-supervised Inference with numerical dependent variables for Means, Covariances and (maybe) Linear Prediction; General Theory |
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Thursday, January 28, 2021 - 9:45am to 10:45am
Title: Bridging Estimation and Decision Making |
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Monday, October 9, 2023 - 4:00pm to 5:00pm
Title: Thresholds |
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Thursday, May 31, 2012 - 9:00am to Saturday, June 2, 2012 - 12:00pm
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Monday, September 23, 2013 - 4:15pm to 5:30pm
Statistical inference of locally stationary spatial processes |
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Monday, November 4, 2019 - 4:00pm to 5:15pm
High-dimensional Tensor Regression Analysis |
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Monday, October 10, 2022 - 4:00pm to 5:00pm
Title: Matching and integration of datasets with low-rank signals and applications in single-cell data analysis |
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Monday, March 27, 2017 - 4:15pm to 5:30pm
Hunting for Earths in Noisy Data |
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Monday, January 19, 2015 - 1:15pm to 2:30pm
Some phase transitions in the stochastic block model |
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Wednesday, February 3, 2021 - 4:00pm to 5:00pm
Title: New Advances in (Adversarially) Robust and Secure Machine Learning |
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Monday, October 23, 2023 - 4:00pm to 5:00pm
Title: Exact Asymptotics with Approximate Message Passing and a Study of the Type 1-Type 2 Error Trade-off for SLOPE |
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Saturday, April 27, 2013 - 9:00am
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Monday, October 14, 2013 - 4:15pm to 5:30pm
Oracle inequalities for computationally adaptive model selection |
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Monday, September 16, 2019 - 4:00pm to 5:15pm
From raw data to stacked particles: data-driven methods for processing of low SNR experimental data in cryo-electron microscopy |
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Monday, December 12, 2022 - 4:00pm to 5:00pm
Title: On Symmetries and Feature Learning in Simple Neural Networks |
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Monday, April 17, 2017 - 4:15pm to 5:30pm
The Curious Role of Mechanistic Explanations in Science and Engineering |
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Monday, January 12, 2015 - 4:15pm to 5:30pm
Optimal Inference After Model Selection |
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Monday, April 19, 2021 - 4:00pm to 5:00pm
Title: A modern take on Huber regression |
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Monday, October 30, 2023 - 4:00pm to 5:00pm
Title: Regularization in Neural Networks: A Probabilistic Perspective |
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Saturday, July 10, 2010 - 9:00am to Sunday, July 11, 2010 - 5:00pm
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Monday, October 21, 2013 - 4:15pm to 5:30pm
Estimating High-dimensional Matrices: Convex Geometry and Computational Barriers |
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Monday, October 28, 2019 - 4:00pm to 5:15pm
Gaussian Differential Privacy |
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Monday, December 19, 2022 - 4:00pm to 5:00pm
Title: Efficient derivative-free Bayesian inference for large-scale inverse problems |
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Monday, March 6, 2017 - 4:15pm to 5:30pm
Translating from Multiple Modalities to Text and Back |
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Monday, January 26, 2015 - 4:15pm to 5:30pm
Statistical and Computational Guarantees for the EM Algorithm |
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Monday, April 5, 2021 - 4:00pm to 5:00pm
Title: Distribution-Free, Risk-Controlling Prediction Sets |
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Monday, October 16, 2023 - 4:00pm to 5:00pm
Title: Learning One-Dimensional Geometry in Random Graphs |
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Thursday, May 14, 2009 - 9:00am to Sunday, May 17, 2009 - 5:00pm
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Monday, November 4, 2013 - 4:15pm to 5:30pm
Exchanging Bips for Flops and Bits: Computational Tradeoffs in Statistical Estimation |
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Monday, November 11, 2019 - 4:00pm to 5:15pm
Mutual information in high-dimensional inference problems |
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Friday, October 14, 2022 - 1:00pm to 4:00pm
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Friday, April 28, 2017 - 11:30am to 12:45pm
Hartigan Lecture: Density contour clusters and spectral clustering |
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Monday, February 2, 2015 - 4:15pm to 5:30pm
High-dimensional low-rank matrix recovery |
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Monday, April 12, 2021 - 4:00pm to 5:00pm
Title: Marginal and multivariate rank-based tests of independence |
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Monday, November 6, 2023 - 4:00pm to 5:00pm
Title: On counterfactual inference with unobserved confounding via exponential family |
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Monday, December 8, 2014 - 4:15pm
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Monday, December 2, 2013 - 4:15pm to 5:30pm
Design and analysis of experiments in the presence of network interference |
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Monday, December 2, 2019 - 4:00pm to 5:15pm
Tensor Decompositions in Machine Learning |
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Monday, April 24, 2023 - 4:00pm to 5:00pm
Convex Analysis at Infinity: An Introduction to Astral Space |
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Monday, October 30, 2017 - 4:15pm to 5:30pm
Statistical Inference for Model Parameters with Stochastic Gradient Descent |
